




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_						 =					logging.get_logger(__name__)

lowerCAmelCase_						 =					{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class 		_A   (    _lowerCamelCase ):
  _UpperCamelCase  :     List[str]					=     'switch_transformers'
  _UpperCamelCase  :     Optional[int]					=     ['past_key_values']
  _UpperCamelCase  :     Optional[int]					=     {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}






  def __init__(    self						:      Optional[Any] ,		_A						:      Tuple=32_128 ,		_A						:      Optional[Any]=768 ,		_A						:      List[str]=64 ,		_A						:      Optional[int]=2_048 ,		_A						:      int=64 ,		_A						:      Any=12 ,		_A						:      Union[str, Any]=3 ,		_A						:      Any=12 ,		_A						:      List[Any]=3 ,		_A						:      Tuple=12 ,		_A						:      int=8 ,		_A						:      Union[str, Any]=False ,		_A						:      Union[str, Any]=0.01 ,		_A						:      Optional[Any]="float32" ,		_A						:      str=False ,		_A						:      Dict=32 ,		_A						:      int=128 ,		_A						:      Tuple=0.1 ,		_A						:      int=1E-6 ,		_A						:      str=0.001 ,		_A						:      Optional[int]=0.001 ,		_A						:      str=1.0 ,		_A						:      List[Any]="relu" ,		_A						:      Union[str, Any]=True ,		_A						:      Tuple=False ,		_A						:      Tuple=True ,		_A						:      Tuple=0 ,		_A						:      int=1 ,		**_A						:      int ,		)						->       str:






       """simple docstring"""







       lowercase   :  Optional[int]        =					vocab_size
       lowercase   :  List[Any]        =					d_model
       lowercase   :  Union[str, Any]        =					d_kv
       lowercase   :  Dict        =					d_ff

       lowercase   :  Optional[Any]        =					num_sparse_encoder_layers

       lowercase   :  Union[str, Any]        =					num_layers
       lowercase   :  Optional[Any]        =					(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       lowercase   :  List[Any]        =					num_sparse_decoder_layers

       # This tells us, each how many encoder layer we'll have to set a sparse layer.
       if self.num_sparse_encoder_layers > 0:
            lowercase   :  Optional[Any]        =					self.num_layers // self.num_sparse_encoder_layers
       else:
            lowercase   :  Dict        =					self.num_layers  # HACK: this will create 0 sparse layers

       # This tells us, each how many encoder layer we'll have to set a sparse layer.
       if self.num_sparse_decoder_layers > 0:
            lowercase   :  Optional[Any]        =					self.num_decoder_layers // self.num_sparse_decoder_layers
       else:
            lowercase   :  str        =					self.num_decoder_layers  # HACK: this will create 0 sparse layers

       lowercase   :  List[str]        =					num_heads
       lowercase   :  List[Any]        =					num_experts
       lowercase   :  Union[str, Any]        =					expert_capacity
       lowercase   :  Optional[int]        =					router_bias
       lowercase   :  Dict        =					router_jitter_noise
       if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}""" )
       lowercase   :  Union[str, Any]        =					router_dtype

       lowercase   :  Optional[Any]        =					router_ignore_padding_tokens
       lowercase   :  List[str]        =					relative_attention_num_buckets
       lowercase   :  List[str]        =					relative_attention_max_distance

       lowercase   :  int        =					dropout_rate
       lowercase   :  Dict        =					layer_norm_epsilon
       lowercase   :  Any        =					initializer_factor
       lowercase   :  Any        =					feed_forward_proj
       lowercase   :  Dict        =					use_cache
       lowercase   :  Union[str, Any]        =					add_router_probs

       lowercase   :  Union[str, Any]        =					router_z_loss_coef
       lowercase   :  Any        =					router_aux_loss_coef

       lowercase   :  Optional[Any]        =					self.feed_forward_proj.split('''-''' )
       lowercase   :  Dict        =					act_info[-1]
       lowercase   :  Optional[int]        =					act_info[0] == '''gated'''

       if len(_A ) > 1 and act_info[0] != "gated" or len(_A ) > 2:
            raise ValueError(
                f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                '''\'gated-gelu\' or \'relu\'''' )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
            lowercase   :  Any        =					'''gelu_new'''

       super().__init__(
           pad_token_id=_A ,		eos_token_id=_A ,		is_encoder_decoder=_A ,		**_A ,		)



def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


